scholarly journals Characterizing the Response of Commercial and Industrial Facilities to Dynamic Pricing Signals From the Utility

2010 ◽  
Author(s):  
Johanna L. Mathieu ◽  
Ashok J. Gadgil ◽  
Duncan S. Callaway ◽  
Phillip N. Price ◽  
Sila Kiliccote
Keyword(s):  
Dynamic Pricing ◽  
Electricity Demand ◽  
Significant Heterogeneity ◽  
Demand Model ◽  
Industrial Facilities ◽  
Dynamic Price ◽  
Price Signals ◽  
Residual Demand ◽  
System Operator ◽  
Peak Pricing

We describe a method to generate statistical models of electricity demand from Commercial and Industrial (C&I) facilities including their response to dynamic pricing signals. Models are built with historical electricity demand data. A facility model is the sum of a baseline demand model and a residual demand model; the latter quantifies deviations from the baseline model due to dynamic pricing signals from the utility. Three regression-based baseline computation methods were developed and analyzed. All methods performed similarly. To understand the diversity of facility responses to dynamic pricing signals, we have characterized the response of 44 C&I facilities participating in a Demand Response (DR) program using dynamic pricing in California (Pacific Gas & Electric’s Critical Peak Pricing Program). In most cases, facilities shed load during DR events but there is significant heterogeneity in facility responses. Modeling facility response to dynamic price signals is beneficial to the Independent System Operator for scheduling supply to meet demand, to the utility for improving dynamic pricing programs, and to the customer for minimizing energy costs.

scholarly journals Dynamic pricing with demand disaggregation for hotel revenue management

2021 ◽  
Author(s):  
Andrei M. Bandalouski ◽  
Natalja G. Egorova ◽  
Mikhail Y. Kovalyov ◽  
Erwin Pesch ◽  
S. Armagan Tarim
Keyword(s):  
Dynamic Pricing ◽  
Programming Model ◽  
Computer Experiments ◽  
Price Policy ◽  
Linear Constraints ◽  
Economic Consequences ◽  
Elastic Model ◽  
Computationally Efficient ◽  
Data Set ◽  
Dynamic Price

AbstractIn this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and linear constraints for dynamic price optimization. The approach is computationally efficient and easy to implement. In computer experiments with a hotel data set, the hotel revenue is increased by about 6% on average in comparison with the actual revenue gained in a past period, where the fixed price policy was employed, subject to an assumption that the demand can deviate from the suggested elastic model. The approach and the developed software can be a useful tool for small hotels recovering from the economic consequences of the COVID-19 pandemic.

Predicting the direction of dynamic price adjustment in the Hong Kong hotel industry

2020 ◽  
pp. 135481662090390
Author(s):  
Ibrahim Mohammed ◽  
Basak Denizci Guillet ◽  
Rob Law ◽  
Wassiuw Abdul Rahaman
Keyword(s):  
Hong Kong ◽  
Dynamic Pricing ◽  
Contextual Factors ◽  
Hotel Industry ◽  
Price Adjustment ◽  
Probit Regression ◽  
Market Conditions ◽  
Negatively Associated ◽  
Dynamic Price ◽  
Price Increases

This study analysed dynamic pricing data of Hong Kong hotels within the last-minute 1-week booking window to determine patterns and direction of room rate changes and their association with hotel characteristics regarding tangible attributes, reputational variables and contextual factors. Findings show that room rates are more likely to increase than decrease or stay constant, and that, holding demand and market conditions constant, the likelihood of price increases (decreases), based on standard binomial probit regression, is positively (negatively) associated with size (tangible attribute), chain affiliation and star rating (reputational attributes), and seller density and location accessibility (contextual factors). These results confirm the importance of differentiation in pricing hotel rooms and indicate how hotel customers and revenue managers can combine these characteristics with predicted demand to anticipate the direction of room rate change in the last-minute booking window as the booking horizon approaches check-in.

scholarly journals Measuring the Intensity of Competition in the Japanese Beef Market

2004 ◽  
Vol 36 (1) ◽  
pp. 113-121 ◽  
Author(s):  
Michael R. Reed ◽  
Sayed H. Saghaian
Keyword(s):  
New Zealand ◽  
Market Power ◽  
Marginal Cost ◽  
Demand Curve ◽  
Market Demand ◽  
Demand Model ◽  
Market Segments ◽  
Residual Demand ◽  
Intensity Of Competition ◽  
Beef Market

A residual demand model for beef exports to Japan is specified and estimated. The objective is to estimate the extent of market power. It is assumed that each exporting country faces a downward-sloping residual demand curve, which reflects the market demand minus the supplies of competitors, and that exporters maximize profit through their output decisions. The analysis is disaggregated by beef cut and form to capture the variation by beef market segments. The results indicate that the highest markup of price over marginal cost belongs to U.S. frozen ribs, the only indication of market power by U.S. exporters. Canada is found to have limited market power, whereas Australia and New Zealand enjoy some market power, including five chilled beef categories.

scholarly journals PRICE DEMAND MODEL FOR A CLOUD CACHE

2010 ◽  
pp. 246-249
Author(s):  
SHAIK MOHAMMED GOUSE ◽  
G. PRAKASH BABU
Keyword(s):  
User Satisfaction ◽  
Dynamic Pricing ◽  
Optimal Pricing ◽  
Demand Model ◽  
Efficient Manner ◽  
Cloud Data ◽  
Pricing Scheme ◽  
Novel Method ◽  
Time Changes ◽  
Price Demand

Cloud applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate price-demand model that enables optimal pricing of query services. The model should be plausible in that it reflects the correlation of cache structures involved in the queries. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This paper proposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in an time-efficient manner. The experimental study shows the efficiency of the solution.

scholarly journals Closed-Loop Elastic Demand Control under Dynamic Pricing Program in Smart Microgrid Using Super Twisting Sliding Mode Controller

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4376 ◽  
Author(s):  
Taimoor Ahmad Khan ◽  
Kalim Ullah ◽  
Ghulam Hafeez ◽  
Imran Khan ◽  
Azfar Khalid ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Dynamic Pricing ◽  
Closed Loop ◽  
Sliding Mode ◽  
Smart Cities ◽  
Sliding Mode Controller ◽  
Elastic Demand ◽  
Demand And Supply ◽  
Dynamic Price ◽  
Demand Control

Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid.

Using water bills to reinforce price signals: evidence from the USA

2005 ◽  
Vol 5 (6) ◽  
pp. 163-171 ◽  
Author(s):  
S. Gaudin
Keyword(s):  
Demand Management ◽  
Management Strategies ◽  
Demand Model ◽  
Price Information ◽  
Price Signals ◽  
Qualitative Variables ◽  
High Population Growth ◽  
The Usa ◽  
History Of Use ◽  
Low Sensitivity

In areas subject to drought and/or high population growth, measures to encourage conservation have become an important part of water management and planning. Residential consumers' low sensitivity to prices reduces the effectiveness and desirability of using price signals as a conservation tool. We hypothesize that consumers' sluggish response to prices is partly due to the fact that price information is not conveniently available to them. If the hypothesis is true, including clear price information on water bills should reinforce consumers' sensitivity to price and therefore increase the power of price-based policies in demand management strategies. A standard aggregate water demand model is augmented with qualitative variables describing the informational content of bills and estimated using a cross section of US utilities. Our results indicate that a utility that spells out unit prices on the water bill can achieve the same level of conservation as others with a thirty to forty percent lower rate increase. We find no evidence that non-price information such as history of use or conservation messages has a significant effect on demand.

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